Method and apparatus for recognizing object

ABSTRACT

A transmission wave is applied to a predetermined range in a width-wise direction of a vehicle. Objects ahead of the vehicle are recognized on the basis of reflected waves resulting from reflections of the transmission wave. Calculation is made as to a position of each of the objects and also a lane-sameness probability for each of the objects that the object and the subject vehicle are on a same lane. Object information pieces corresponding to the respective objects represent the calculated positions of the objects and the calculated lane-sameness probabilities for the objects. In cases where at least two objects become substantially equal in position, the two objects are recognized as a single object. One is selected from the two objects which relates to a calculated lane-sameness probability equal to or higher than a predetermined value. The single object takes over an object information piece corresponding to the selected object.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to a method of recognizing an object. Inaddition, this invention relates to an apparatus for recognizing anobject which can be mounted on a vehicle. Furthermore, this inventionrelates to a recording medium storing a computer program for recognizingan object.

2. Description of the Related Art

A known object recognition apparatus for a vehicle emits a forward wavebeam such as a light beam or a millimeter wave beam from the body of thevehicle, and enables the forward wave beam to scan a given angularregion in front of the body of the vehicle. In the case where an objectexists in the given angular region, the forward wave beam encounters theobject before being at least partially reflected thereby. A portion ofthe reflected wave beam returns to the apparatus as an echo wave beam.The apparatus detects and recognizes the object in response to the echowave beam.

The known object recognition apparatus is used in a warning system for avehicle which alarms when an obstacle such as a preceding vehicle existsin a given angular region in front of the present vehicle. The knownobject recognition apparatus is used also in a system for a vehiclewhich controls the speed of the vehicle to maintain a proper distancebetween the vehicle and a preceding vehicle.

Japanese patent application publication number 8-240660 discloses anon-vehicle apparatus for recognizing objects. The apparatus in Japaneseapplication 8-240660 includes a distance sensor mounted on the presentvehicle which detects the longitudinal-direction andtransverse-direction distances to objects from the present vehicle. Thedistance sensor generates detection data having pieces representingdistances to objects respectively. The distances to the objects meanpositions of the objects relative to the present vehicle. In theapparatus of Japanese application 8-240660, pieces of detection datawhich correspond to object positions close to each other and in a firstprescribed mutual-distance range are collected into a block having ablock label. Generally, there are a plurality of blocks. Speeds relativeto the present vehicle and corresponding to respective blocks arecalculated. Blocks which correspond to positions in a second prescribedmutual-distance range, and which correspond to speeds in a presetspeed-difference range are collected into a group having a group label.Finally, an object is recognized from detection data pieces representinga group.

Japanese patent application publication number 11-337636 discloses arear monitoring system for a vehicle. In the system of Japaneseapplication 11-337636, a rear sensor outputs a plurality of wave motionsfor detection from the rear of one's own vehicle toward differentregions, and captures the reflected waves in response to each of thewave motions. The location of a wave motion reflecting point in the rearof one's own vehicle is detected, and an object in the rear of one's ownvehicle is discriminated by an object discriminating means on the basisof the continuity of information on the location of a wave motionreflecting point. Then, the relative speed of the object discriminatedby the object discriminating means with respect to one's own vehicle iscomputed by deciding means. On the basis of the relative speed, it isdetermined whether or not the object is an approaching object. Whenthere are a plurality of discriminated objects at this time, thedistance between the two objects is compared with a reference distanceobtained by multiplying the speed of one's own vehicle by apredetermined time. When the distance between the two objects is equalto or less than the reference distance, the two objects are decided tobe the one and the same object. Thus, in this case, the two objects arerecognized as a single object at the object discriminating means.

Japanese patent application publication number 9-15331 discloses anon-vehicle apparatus for detecting an object. The apparatus in Japaneseapplication 9-15331 includes a distance sensor which detects a distancebetween its own vehicle and an object by transmission and reception oflaser light. Output data from the distance sensor are developed by acoordinate development means on the X-Y coordinates for which thelongitudinal direction from the own vehicle is taken as the Y axis andthe lateral direction as the X axis. A cell forming means which providesa plurality of cells divided at prescribed intervals in the directionsof the X and Y axes sets the developed data on the cells, and outputsthe X-Y coordinates and the number of the data of each cell as cellinformation. Based on this information, an object discriminating meansattaches the same label to the cells near to each other, anddiscriminates a plurality of cells as the same object. Then, a settingnumber of data being closer in the distance in the longitudinaldirection are selected out of the cell data corresponding to the sameobject, and the longitudinal-direction distances corresponding to theselected data are averaged into a mean value. The mean value is used asan indication of the distance in the longitudinal direction from the ownvehicle to the object.

U.S. Pat. No. 5,710,565 discloses an inter-vehicle distance controlsystem which includes a laser scanning type distance sensor for moving alaser beam in a width-wise direction of a system vehicle to implementscanning and to determine relative positions and relative angles ofobjects within a forward detectable zone. A determination is made as tosame lane probabilities that the objects exist in the same lane of aroad as the system vehicle on the basis of a variable probabilitydistribution and the relative positions and the relative angles of theobjets. A target preceding vehicle is selected from the objects on thebasis of the same lane probabilities. Information of the targetpreceding vehicle is used in controlling the speed of the system vehicleto keep constant the distance to the target preceding vehicle.

U.S. Pat. No. 5,574,463 discloses an obstacle recognition system for avehicle which includes a radar device for emitting a wave beam into agiven angular range outside a vehicle, and scanning the given angularrange by the wave beam. The radar device detects a reflected wave beam.A recognizing device is operative for recognizing an obstacle withrespect to the vehicle on the basis of the result of detection of thereflected wave beam by the radar device. In the recognizing device, apoint recognizing section recognizes obstacles as points, and a unitingsection is operative for uniting adjacent points among the pointsprovided by the point recognizing section. The uniting section providessets each having adjacent points. A line-segment recognizing section isoperative for detecting a specific set or specific sets of adjacentpoints among the adjacent-point sets provided by the uniting section,and for recognizing every detected specific set as a line segment havinga length only along a width direction of the vehicle. Every specific sethas a length smaller than a given length along a longitudinal directionof the vehicle. A position estimating section estimates the position ofa line segment, which will be provided by the line-segment recognizingsection, in response to the position of a previously-provided linesegment. An identity judging section is operative for comparing theline-segment position estimated by the position estimating section andthe position of a line segment currently provided by the line-segmentrecognizing section to judge whether or not the line segment currentlyprovided by the line-segment recognizing section and thepreviously-provided line segment are the same.

It is known to use target models in object recognition for a vehicle. Insome cases, there simultaneously occur a correct target model and awrong target model as a result of recognition concerning one object. Thewrong target model is caused by, for example, noise. Generally, everytarget model has object information. An example of the objectinformation includes a piece representing the center position of anobject, a piece representing the size of the object, and a piecerepresenting the speed of the object relative to the vehicle. When twotarget models become positionally coincident with each other, one ofthem is selected as an effective target model and the other is deleted.Thus, only the object information related to the selected target modelcontinues to be effective. An example of conditions of the selection isas follows. Measurement is given of a first time interval during which afirst target model is continuously detected, and a second time intervalduring which a second target model is continuously detected. In the casewhere the first and second target models become positionally coincidentwith each other, the first and second measured time intervals arecompared with each other to decide which of them is longer (or which ofthem is shorter). Then, one of the first and second target models whichcorresponds to the longer measured time interval is selected as aneffective target model. This selection is based on the idea that one ofthe first and second target models which corresponds to the longermeasured time interval agrees with a correct target model while theother agrees with a wrong target model.

There is a chance that under certain circumstances, one of the first andsecond target models which corresponds to the shorter measured timeinterval agrees with a correct target model while the other agrees witha wrong target model. In these circumstances, the correct target modelis deleted, and the wrong target model is selected. The deletion of thecorrect target model reduces the accuracy of object recognition.

SUMMARY OF THE INVENTION

It is a first object of this invention to provide a method of accuratelyrecognizing an object.

It is a second object of this invention to provide an apparatus foraccurately recognizing an object.

It is a third object of this invention to provide a recording mediumstoring a computer program for accurately recognizing an object.

A first aspect of this invention provides a method of applying atransmission wave to a predetermined range in a width-wise direction ofa subject vehicle, and recognizing objects located ahead of the subjectvehicle on the basis of reflected waves which result from reflections ofthe transmission wave. The method comprises the steps of calculatingpositions of the objects; calculating a lane-sameness probability foreach of the objects that the object and the subject vehicle are on asame lane; generating object information pieces corresponding to theobjects respectively, the object information pieces representing thecalculated positions of the objects and the calculated lane-samenessprobabilities for the objects; determining whether or not at least twoobjects among the objects become substantially equal in position; incases where it is determined that at least two objects becomesubstantially equal in position, recognizing the at least two objects asa single object; selecting one from the at least two objects whichrelates to a calculated lane-sameness probability equal to or higherthan a predetermined value; and causing said single object to take overan object information piece corresponding to the selected object.

A second aspect of this invention provides a method of applying atransmission wave to a predetermined range in a width-wise direction ofa subject vehicle, and recognizing objects located ahead of the subjectvehicle on the basis of reflected waves which result from reflections ofthe transmission wave. The method comprises the steps of calculatingpositions of the objects; determining whether or not a recognition stateof each of the objects is stable; generating object information piecescorresponding to the objects respectively, the object information piecesrepresenting the calculated positions of the objects and whether or notthe recognition states of the objects are stable; determining whether ornot at least two objects among the objects become substantially equal inposition; in cases where it is determined that at least two objectsbecome substantially equal in position, recognizing the at least twoobjects as a single object; selecting one from the at least two objectswhose recognition state is determined to be stable; and causing saidsingle object to take over the object information piece corresponding tothe selected object.

A third aspect of this invention provides a method of applying atransmission wave to a predetermined range in a width-wise direction ofa subject vehicle, and recognizing objects located ahead of the subjectvehicle on the basis of reflected waves which result from reflections ofthe transmission wave. The method comprises the steps of periodicallycalculating positions of the objects; estimating current positions ofthe objects on the basis of previously calculated positions thereof;calculating deviations between the estimated current positions of theobjects and currently calculated positions thereof; generating objectinformation pieces corresponding to the objects respectively, the objectinformation pieces representing the calculated positions of the objectsand the calculated deviations related to the objects; determiningwhether or not at least two objects among the objects becomesubstantially equal in position; in cases where it is determined that atleast two objects become substantially equal in position, recognizingthe at least two objects as a single object; selecting one from the atleast two objects which relates to a smallest calculated deviation; andcausing said single object to take over the object information piececorresponding to the selected object.

A fourth aspect of this invention provides an object recognitionapparatus comprising radar means for applying a transmission wave to apredetermined range in a width-wise direction of a subject vehicle, anddetecting objects on the basis of reflected waves which result fromreflections of the transmission wave; and recognizing means forrecognizing objects located ahead of the subject vehicle on the basis ofresults of detection by the radar means. The recognizing meanscomprises 1) first means for calculating positions of the recognizedobjects; 2) second means for calculating a lane-sameness probability foreach of the recognized objects that the object and the subject vehicleare on a same lane; 3) third means for generating object informationpieces corresponding to the recognized objects respectively, the objectinformation pieces representing the calculated positions of therecognized objects and the calculated lane-sameness probabilities forthe recognized objects; 4) fourth means for determining whether or notat least two objects among the recognized objects become substantiallyequal in position; 5) fifth means for, in cases where the fourth meansdetermines that at least two objects become substantially equal inposition, recognizing the at least two objects as a single object; 6)sixth means for selecting one from the at least two objects whichrelates to a calculated lane-sameness probability equal to or higherthan a predetermined value; and 7) seventh means for causing said singleobject to take over an object information piece corresponding to theobject selected by the sixth means.

A fifth aspect of this invention provides an object recognitionapparatus comprising radar means for applying a transmission wave to apredetermined range in a width-wise direction of a subject vehicle, anddetecting objects on the basis of reflected waves which result fromreflections of the transmission wave; and recognizing means forrecognizing objects located ahead of the subject vehicle on the basis ofresults of detection by the radar means. The recognizing meanscomprises 1) first means for calculating positions of the recognizedobjects; 2) second means for determining whether or not a recognitionstate of each of the recognized objects is stable; 3) third means forgenerating object information pieces corresponding to the recognizedobjects respectively, the object information pieces representing thecalculated positions of the recognized objects and whether or not therecognition states of the recognized objects are stable; 4) fourth meansfor determining whether or not at least two objects among the recognizedobjects become substantially equal in position; 5) fifth means for, incases where the fourth means determines that at least two objects becomesubstantially equal in position, recognizing the at least two objects asa single object; 6) sixth means for selecting one from the at least twoobjects whose recognition state is determined to be stable; and 7)seventh means for causing said single object to take over the objectinformation piece corresponding to the object selected by the sixthmeans.

A sixth aspect of this invention provides an object recognitionapparatus comprising radar means for applying a transmission wave to apredetermined range in a width-wise direction of a subject vehicle, anddetecting objects on the basis of reflected waves which result fromreflections of the transmission wave; and recognizing means forrecognizing objects located ahead of the subject vehicle on the basis ofresults of detection by the radar means. The recognizing meanscomprises 1) first means for periodically calculating positions of therecognized objects; 2) second means for estimating current positions ofthe recognized objects on the basis of previously calculated positionsthereof; 3) third means for calculating deviations between the estimatedcurrent positions of the recognized objects and currently calculatedpositions thereof; 4) fourth means for generating object informationpieces corresponding to the recognized objects respectively, the objectinformation pieces representing the calculated positions of therecognized objects and the calculated deviations related to therecognized objects; 5) fifth means for determining whether or not atleast two objects among the recognized objects become substantiallyequal in position; 6) sixth means for, in cases where the fifth meansdetermines that at least two objects become substantially equal inposition, recognizing the at least two objects as a single object; 7)seventh means for selecting one from the at least two objects whichrelates to a smallest calculated deviation; and 8) eighth means forcausing said single object to take over the object information piececorresponding to the object selected by the seventh means.

A seventh aspect of this invention is based on the fourth aspectthereof, and provides an object recognition apparatus wherein therecognizing means further comprises eighth means for determining whetheror not a recognition state of each of the recognized objects is stable;ninth means for adding results of the determining by the eighth means tothe object information pieces; tenth means for, either in cases where aplurality of objects among the at least two objects relate to calculatedlane-sameness probabilities equal to or higher than the predeterminedvalue or in cases where a plurality of objects among the at least twoobjects relate to calculated lane-sameness probabilities less than thepredetermined value, selecting one from the at least two objects whoserecognition state is determined to be stable; and eleventh means forcausing said single object to take over the object information piececorresponding to the object selected by the tenth means.

An eighth aspect of this invention is based on the fourth aspectthereof, and provides an object recognition apparatus wherein therecognizing means further comprises eighth means for estimating currentpositions of the recognized objects on the basis of previouslycalculated positions thereof; ninth means for calculating deviationsbetween the estimated current positions of the recognized objects andcurrently calculated positions thereof; tenth means for adding thedeviations calculated by the ninth means to the object informationpieces; eleventh means for, either in cases where a plurality of objectsamong the at least two objects relate to calculated lane-samenessprobabilities equal to or higher than the predetermined value or incases where a plurality of objects among the at least two objects relateto calculated lane-sameness probabilities less than the predeterminedvalue, selecting one from the at least two objects which relates to asmallest calculated deviation; and twelfth means for causing said singleobject to take over the object information piece corresponding to theobject selected by the eleventh means.

A ninth aspect of this invention is based on the fourth aspect thereof,and provides an object recognition apparatus wherein the recognizingmeans further comprises eighth means for determining whether or not arecognition state of each of the recognized objects is stable; ninthmeans for adding results of the determining by the eighth means to theobject information pieces; tenth means for estimating current positionsof the recognized objects on the basis of previously calculatedpositions thereof; eleventh means for calculating deviations between theestimated current positions of the recognized objects and currentlycalculated positions thereof; twelfth means for adding the deviationscalculated by the eleventh means to the object information pieces;thirteenth means for, either in cases where a plurality of objects amongthe at least two objects relate to calculated lane-samenessprobabilities equal to or higher than the predetermined value and onlyone of the at least two objects has a recognition state determined to bestable or in cases where a plurality of objects among the at least twoobjects relate to calculated lane-sameness probabilities less than thepredetermined value and only one of the at least two objects has arecognition state determined to be stable, selecting one from the atleast two objects whose recognition state is determined to be stable;fourteenth means for causing said single object to take over the objectinformation piece corresponding to the object selected by the thirteenthmeans; fifteenth means for, either in cases where a plurality of objectsamong the at least two objects relate to calculated lane-samenessprobabilities equal to or higher than the predetermined value and two ormore of the at least two objects have recognition states determined tobe stable or in cases where a plurality of objects among the at leasttwo objects relate to calculated lane-sameness probabilities less thanthe predetermined value and two or more of the at least two objects haverecognition states determined to be stable, selecting one from the atleast two objects which relates to a smallest calculated deviation; andsixteenth means for causing said single object to take over the objectinformation piece corresponding to the object selected by the fifteenthmeans.

A tenth aspect of this invention is based on the fifth aspect thereof,and provides an object recognition apparatus wherein the recognizingmeans further comprises eighth means for estimating current positions ofthe recognized objects on the basis of previously calculated positionsthereof; ninth means for calculating deviations between the estimatedcurrent positions of the recognized objects and currently calculatedpositions thereof; tenth means for adding the deviations calculated bythe ninth means to the object information pieces; eleventh means for,either in cases where a plurality of objects among the at least twoobjects relate to calculated lane-sameness probabilities equal to orhigher than the predetermined value and two or more of the at least twoobjects have recognition states determined to be stable or in caseswhere a plurality of objects among the at least two objects relate tocalculated lane-sameness probabilities less than the predetermined valueand two or more of the at least two objects have recognition statesdetermined to be stable, selecting one from the at least two objectswhich relates to a smallest calculated deviation; and twelfth means forcausing said single object to take over the object information piececorresponding to the object selected by the eleventh means.

An eleventh aspect of this invention is based on the fifth aspectthereof, and provides an object recognition apparatus wherein the secondmeans in the recognizing means comprises means for calculating anacceleration of each of the recognized objects relative to the subjectvehicle, means for judging whether or not the calculated acceleration isin a predetermined range hardly occurring under usual trafficconditions, means for, when the calculated acceleration is judged to bein the predetermined range, determining that a recognition state of therelated object is not stable, and means for, when the calculatedacceleration is judged to be not in the predetermined range, determiningthat a recognition state of the related object is stable.

A twelfth aspect of this invention provides a recording medium storing aprogram for controlling a computer operating as the recognizing means inthe object recognition apparatus of the fourth aspect of this invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a vehicle control apparatus according to afirst embodiment of this invention.

FIG. 2 is an operation flow diagram of an electronic control unit (ECU)in FIG. 1.

FIG. 3 is a flowchart of a portion of a program for the ECU in FIG. 1.

FIG. 4 is a diagram of an example of detected point-like object parts,and segments which result from unifying close ones of the detectedpoint-like object parts.

FIG. 5 is a flowchart of a block in FIG. 3.

FIG. 6 is a diagram of the immediately-previous position, the estimatedcurrent position, and the relative speed of a target model, and anestimated arrival zone centered at the estimated current position.

FIG. 7 is a flowchart of a first block in FIG. 5.

FIG. 8 is a flowchart of a second block in FIG. 5.

FIG. 9 is an example of target models to be merged.

FIG. 10 is a diagram of target models, and distance deviations relatedthereto.

FIG. 11 is a diagram of conversion of coordinates.

FIG. 12 is a diagram of a map for determining an instantaneouslane-sameness probability which is separated into regions.

FIG. 13 is a diagram of a map representing a relation between aparameter “α” and a distance Z.

DETAILED DESCRIPTION OF THE INVENTION First Embodiment

FIG. 1 shows a vehicle control apparatus according to a first embodimentof this invention. The vehicle control apparatus is mounted on avehicle. The vehicle control apparatus alarms when an obstacle in aspecified condition exists in a given angular region (a given detectionarea) in front of the present vehicle. The vehicle control apparatusadjusts the speed of the present vehicle in accordance with the speed ofa preceding vehicle. The vehicle control apparatus includes a recordingmedium.

As shown in FIG. 1, the vehicle control apparatus includes an electroniccontrol unit (ECU) 3 having a computer such as a microcomputer. Thecomputer in the ECU 3 has a combination of an input/output (I/O)interface, a CPU, a ROM, and a RAM. The ECU 3 (the computer therein)operates in accordance with a program stored in the ROM. The program maybe stored in the RAM. In this case, the RAM is provided with a backupdevice.

Alternatively, the program may be stored in a recording medium such as afloppy disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, or a harddisk. In this case, the ECU 3 is connected with a drive for therecording medium, and the program is downloaded into the computer of theECU 3 through the drive.

The vehicle control apparatus includes a laser radar sensor 5, a vehiclespeed sensor 7, a brake switch 9, and a throttle opening degree sensor(a throttle position sensor) 11 which are connected to the ECU 3. Theoutput signals of the devices 5, 7, 9, and 11 are inputted into the ECU3.

The vehicle control apparatus includes an alarm sound generator 13, adistance indicator 15, a sensor failure indicator 17, a brake drivedevice 19, a throttle drive device 21, and an automotive automatictransmission control device 23 which are connected to the ECU 3. The ECU3 outputs drive signals to the devices 13, 15, 17, 19, 21, and 23.

The vehicle control apparatus includes an alarm sound volume settingdevice 24, an alarm sensitivity setting device 25, a cruise controlswitch 26, a steering sensor 27, and a yaw rate sensor 28 which areconnected to the ECU 3. The output signals of the devices 24, 25, 26,27, and 28 are inputted into the ECU 3. The alarm sound volume settingdevice 24 acts to set the volume of alarm sound. The alarm sensitivitysetting device 25 acts to set the sensitivity in a warning determinationprocess mentioned later.

The vehicle control apparatus includes a power supply switch 29connected to the ECU 3. When the power supply switch 29 is changed toits on position, the ECU 3 is powered and starts predeterminedprocesses.

The laser radar sensor 5 has a transmitting and receiving portion, and adistance and angle calculating portion. The transmitting and receivingportion emits a forward laser beam ahead of the present vehicle, andcontrols the forward laser beam to periodically scan a given angularregion in front of the present vehicle. The given angular regioncorresponds to a given sectorial detection area monitored by thetransmitting and receiving portion. In the case where an object existsin the detection area (the given angular region), the forward laser beamencounters the object before being at least partially reflected thereby.A portion of the reflected laser beam returns to the transmitting andreceiving portion as an echo laser beam. The transmitting and receivingportion receives the echo laser beam, and converts the echo laser beaminto a corresponding electric signal. The transmitting and receivingportion outputs the electric signal to the distance and anglecalculating portion. The distance and angle calculating portion detectsthe angle (the angular position) “θ” of the object in response to theoutput signal from the transmitting and receiving portion. The distanceand angle calculating portion measures the time interval between themoment of the transmission of a forward laser beam and the moment of thereception of a related echo laser beam in response to the output signalfrom the transmitting and receiving portion. The distance and anglecalculating portion detects the distance “r” to the object from thepresent vehicle on the basis of the measured time interval. The distanceand angle calculating portion informs the ECU 3 of the angle (theangular position) “θ” of the object and the distance “r” thereto. Ingeneral, since the object is greater than the cross-sectional area ofthe forward laser beam and is scanned thereby, the distance and angleinformation notified from the distance and angle calculating portion tothe ECU 3 relates to a partial object or a point-like part of an object.Objects detected by the laser radar sensor 5 include obstacles withrespect to the present vehicle.

During every scanning period (every frame period), the angular directionof the forward laser beam is changed a unit-angle by a unit-angle. Theunit angle corresponds to, for example, 0.15 degrees. The detection area(the given angular region) scanned by the forward laser beam has anangular range of, for example, about 16 degrees which extends in thewidth-wise direction of the present vehicle as viewed therefrom. In thiscase, the detection area corresponds to 105 image points or pixels (105multiplied by 0.15 degrees equals about 16 degrees) composing one frame.

The laser beam may be replaced by a radio wave beam, a millimeter wavebeam, or an ultrasonic beam. The scanning may be implemented bycontrolling the echo beam reception by the transmitting and receivingportion in the laser radar sensor 5.

The ECU 3 receives the measurement data (the distance and angleinformation) from the laser radar sensor 5. The ECU 3 recognizes objectson the basis of the measurement data. The ECU 3 detects a precedingvehicle with respect to the present vehicle on the basis of the resultof the object recognition. In addition, the ECU 3 detects conditions ofthe preceding vehicle. The ECU 3 executes inter-vehicle distancecontrol. During the execution of the inter-vehicle distance control, theECU 3 generates and outputs suitable drive signals to the brake driveunit 19, the throttle drive device 21, and the automotive automatictransmission control device 23 to adjust the speed of the presentvehicle in accordance with the conditions of the preceding vehicle.Simultaneously with the execution of the inter-vehicle distance control,the ECU 3 executes a warning determination process designed to generatean alarm in the case where an obstacle corresponding to a recognizedobject remains in a specified area during longer than a prescribed timeinterval. The obstacle corresponds to, for example, a preceding vehicle,a stationary vehicle, a guardrail on a road side, or a prop on a roadside.

The vehicle speed sensor 7 is associated with a wheel of the presentvehicle. The vehicle speed sensor 7 detects the rotational speed of thevehicle wheel. The vehicle speed sensor 7 outputs a signal to the ECU 3which represents the detected rotational speed of the vehicle wheel.

The steering sensor 27 detects the degree of operation of a vehiclesteering wheel (not shown), that is, the steering angle in the presentvehicle. Specifically, the steering sensor 27 detects a quantity ofchange of the steering angle. The steering sensor 27 outputs a signal tothe ECU 3 which represents the detected quantity of change of thesteering angle. When the power supply switch 29 is moved to its onposition, a variable used in the ECU 3 as an indication of a detectedsteering angle “θ” (radian) is initialized to “0”. After the movement ofthe power supply switch 29 to its on position, the detected steeringangle “θ” is decided by integrating the quantity of change of thesteering angle which is represented by the output signal of the steeringsensor 27.

The yaw rate sensor 28 detects the rate Ω (radian/second) of change inthe rotational angle (the yaw angle) of the body of the present vehicleabout the vertical axis thereof. The yaw rate sensor 28 informs the ECU3 of the detected yaw rate Ω.

When the cruise control switch 26 is changed to its on position, the ECU3 operates to start the vehicle cruise control. During the execution ofthe vehicle cruise control, signal processing for the inter-vehicledistance control can be implemented by the ECU 3. When the ECU 3determines that the present vehicle is excessively close to an objectivepreceding vehicle, the alarm sound generator 13 is activated by the ECU3 to generate alarm sound.

The volume of the generated alarm sound is equal to a level adjustablydetermined by the alarm sound volume setting device 24. The sensitivityof generation of alarm sound can be adjusted by the alarm sensitivitysetting device 25.

The brake switch 9 detects depression of a brake pedal of the presentvehicle. The brake switch 9 informs the ECU 3 of the detectedbrake-pedal depression. The ECU 3 generates a drive signal for the brakedrive device 19 in response to information containing the information ofthe detected brake-pedal depression. The ECU 3 outputs the generateddrive signal to the brake drive device 19. The brake drive device 19adjusts the braking pressure in response to the drive signal outputtedfrom the ECU 3.

The throttle opening degree sensor 11 detects the degree of openingthrough a vehicular engine throttle valve. The throttle opening degreesensor 11 outputs a signal to the ECU 3 which represents the detectedthrottle opening degree. The ECU 3 controls the throttle drive device 21in response to the detected throttle opening degree, thereby adjustingthe actual degree of opening through the throttle valve and adjustingthe power output of the engine.

The ECU 3 determines whether or not the laser radar sensor 5 isoperating normally by referring to the output signal therefrom. When theECU 3 determines that the laser radar sensor 5 is not operatingnormally, the sensor failure indicator 17 is controlled by the ECU 3 toindicate a failure.

The ECU 3 selects an objective preceding vehicle from among candidatepreceding vehicles detected in response to the output signal of thelaser radar sensor 5. The ECU 3 calculates the distance to the objectivepreceding vehicle from the present vehicle. The distance indicator 15 iscontrolled by the ECU 3 to indicate the calculated distance to theobjective preceding vehicle from the present vehicle.

The automotive automatic transmission control device 23 selects a usedgear position of an automotive automatic transmission and therebycontrols the speed of the present vehicle in response to the outputsignal from the ECU 3.

FIG. 2 shows the flow of operation of the ECU 3 rather than the hardwarestructure thereof. With reference to FIG. 2, an object recognition block43 receives, from the distance and angle calculating portion in thelaser radar sensor 5, measurement data representing a distance “r” andan angle “θ” concerning each detected object (each detected partialobject or each detected point-like object part). The object recognitionblock 43 converts the distance and angle data of polar coordinates intomeasurement data of X-Z orthogonal coordinates designed so that theorigin (0, 0) coincides with the center of a laser radar formed by thesensor 5, and the X axis and the Z axis coincide with a width-wisedirection and a longitudinal forward direction of the present vehiclerespectively. The object recognition block 43 groups detected partialobjects (detected point-like object parts) represented by theorthogonal-coordinate measurement data into sets or segmentscorresponding to detected complete objects respectively. The groupingand the segments will be described later. Pieces of thegrouping-resultant segment data which indicate respective segments areobject-unit data pieces (per-object data pieces). A model of a completeobject which is represented by central position data, size data,relative-speed data, and stationary-moving determination result data(recognition type data) will be called a target model.

A vehicle speed calculation block 47 computes the speed V of the presentvehicle on the basis of the output signal from the vehicle speed sensor7.

The object recognition block 43 calculates the central position (X, Z)and size (W, D) of each detected complete object on the basis of thegrouping-resultant segment data. Here, W denotes a transverse width, andD denotes a depth. The object recognition block 43 calculates the speed(Vx, Vz) of the complete object relative to the present vehicle from atime-domain variation in the central position (X, Z) thereof. The objectrecognition block 43 is informed of the speed V of the present vehicleby the vehicle speed calculation block 47. The object recognition block43 determines whether or not each detected complete object is stationaryor moving on the basis of the vehicle speed V and the relative speed(Vx, Vz). One or more which may affect the travel of the present vehicleare selected from detected complete objects on the basis of thestationary-moving determination results and the central positions of thedetected complete objects. Information of the distance to each selectedcomplete object is transferred to the distance indicator 15 so that thedistance to the selected complete object is indicated by the distanceindicator 15.

A sensor failure detection block 44 receives the output data (theobject-recognition result data) from the object recognition block 43which represent the object parameters calculated thereby. The sensorfailure detection block 44 determines whether the output data from theobject recognition block 43 are in a normal range or an abnormal range.When the output data from the object recognition block 43 are in theabnormal range, the sensor failure detection block 44 activates thesensor failure indicator 17 to indicate a failure.

A steering angle calculation block 49 computes the steering angleregarding the present vehicle on the basis of the output signal from thesteering sensor 27. A yaw rate calculation block 51 computes the yawrate of the present vehicle on the basis of the output signal from theyaw rate sensor 28.

A curvature-radius calculation block 57 is informed of the vehicle speedV by the vehicle speed calculation block 47. The curvature-radiuscalculation block 57 is informed of the computed steering angle by thesteering angle calculation block 49. The curvature-radius calculationblock 57 is informed of the computed yaw rate by the yaw ratecalculation block 51. The curvature-radius calculation block 57 computesthe radius R of curvature of the road on the basis of the vehicle speedV, the steering angle, and the yaw rate. The curvature-radiuscalculation block 57 informs the object recognition block 43 of thecomputed curvature radius R.

The object recognition block 43 detects preceding vehicles among thedetected complete objects by referring to the central positions, thesizes, the relative speeds, and the recognition types thereof. Theobject recognition block 43 computes the probability (the lane-samenessprobability) P that the lanes along which the present vehicle and eachpreceding vehicle (or each detected complete object) are travelingrespectively are the same on the basis of the curvature radius R and thecentral position and size of the preceding vehicle (or the detectedcomplete object).

A preceding-vehicle determination block 53 is informed of thelane-sameness probability P for each preceding vehicle (each detectedcomplete object) by the object recognition block 43. In addition, thepreceding-vehicle determination block 53 is informed of the centralposition, the size, the relative speed, and the recognition type of eachdetected complete object by the object recognition block 43. Thepreceding-vehicle determination block 53 detects an objective precedingvehicle on the basis of the lane-sameness probabilities P, the centralpositions, the sizes, the relative speeds, and the recognition types ofthe detected complete objects. Specifically, the preceding-vehicledetermination block 53 selects an objective preceding vehicle fromcandidate complete objects (candidate preceding vehicles) in response tothe lane-sameness probabilities P, the central positions, the sizes, therelative speeds, and the recognition types of the detected completeobjects.

An inter-vehicle distance control and warning determination block 55 isinformed of the distance Z to the objective preceding vehicle and therelative speed Vz of the objective preceding vehicle by thepreceding-vehicle determination block 53. The inter-vehicle distancecontrol and warning determination block 55 is informed of the vehiclespeed V by the vehicle speed calculation block 47. The inter-vehicledistance control and warning determination block 55 detects settingconditions of the cruise control switch 26 from the output signalthereof. The inter-vehicle distance control and warning determinationblock 55 detects the state of the brake switch 9 from the output signalthereof. The state of the brake switch 9 represents whether or not thevehicle brake pedal is depressed. The inter-vehicle distance control andwarning determination block 55 is informed of the degree of openingthrough the vehicular engine throttle valve by the throttle openingdegree sensor 11. The inter-vehicle distance control and warningdetermination block 55 is informed of the alarm volume setting value bythe alarm sound volume setting device 24. The inter-vehicle distancecontrol and warning determination block 55 is informed of the alarmsensitivity setting value by the alarm sensitivity setting device 25.The inter-vehicle distance control and warning determination block 55implements a warning determination and a cruise determination inresponse to the distance Z to the objective preceding vehicle, therelative speed Vz of the objective preceding vehicle, the vehicle speedV, the setting conditions of the cruise control switch 26, the state ofthe brake switch 9, the throttle opening degree, and the alarmsensitivity setting value. During the warning determination, theinter-vehicle distance control and warning determination block 55determines whether or not an alarm should be generated. During thecruise determination, the inter-vehicle distance control and warningdetermination block 55 determines the contents of vehicle speed control.When it is determined that an alarm should be generated, theinter-vehicle distance control and warning determination block 55outputs an alarm generation signal to the alarm sound generator 13. Inthis case, the alarm sound generator 13 produces alarm sound. Theinter-vehicle distance control and warning determination block 55adjusts the level of the alarm sound in accordance with the sound volumeset by the alarm sound volume setting device 24. In the case where thecruise determination corresponds to the execution of cruise control, theinter-vehicle distance control and warning determination block 55outputs suitable control signals to the automotive automatictransmission control device 23, the brake drive device 19, and thethrottle drive device 21. During the execution of the warning controland the cruise control, the inter-vehicle distance control and warningdetermination block 55 outputs an indication signal to the distanceindicator 15 to inform the vehicle's driver of distance-relatedconditions.

As previously mentioned, the ECU 3 operates in accordance with a programstored in its internal ROM or RAM. FIG. 3 is a flowchart of a portion ofthe program for the ECU 3 which relates to object recognition. Theprogram portion in FIG. 3 is repetitively executed at a periodcorresponding to the period of the scanning implemented by the laserradar sensor 5.

As shown in FIG. 3, a first step S1 of the program portion receivesdistance and angle measurement data from the laser radar sensor 5 forone period of the scanning. In other words, the step S1 receivesdistance and angle measurement data corresponding to one frame. Thescanning period is equal to, for example, 100 msec.

A step S2 following the step S1 converts the distance and angle data ofpolar coordinates into measurement data of X-Z orthogonal coordinates.The orthogonal-coordinate measurement data represent detected partialobjects or detected point-like object parts. The step S2 groups thedetected point-like object parts (the detected partial objects) intosegments corresponding to detected complete objects respectively.

With reference to FIG. 4, the step S2 searches the detected point-likeobject parts for close ones which are spaced by X-axis-directiondistances ΔX of 0.2 m or less and Z-axis-direction distances ΔZ of 2 mor less. The step S2 combines or unifies the close point-like objectparts into a segment (a set) corresponding to a detected completeobject. There can be a plurality of segments. The step S2 generates datarepresenting segments which are referred to as segment data.Specifically, one segment data piece (one data piece representing asegment) generated by the step S2 corresponds to a rectangular regionhaving two sides parallel to the X axis and two sides parallel to the Zaxis. One segment data piece contains an information piece indicatingthe central position of the related segment, an information pieceindicating the size (W, D) of the segment, an information pieceindicating the coordinates of the right-hand edge of the segment, andthe coordinates of the left-hand edge of the segment.

With reference back to FIG. 3, a block S3 subsequent to the step S2generates target models from the segment data pieces provided by thestep S2. After the block S3, the current execution cycle of the programportion ends.

As shown in FIG. 5, the block S3 has a step S31 following the step S2 inFIG. 3. The step S31 searches for segment data pieces corresponding totarget models. Specifically, the step S31 handles target models whichhave been generated at or before the immediately-previous executioncycle of the program portion. Also, the step S31 handles the segmentdata pieces generated by the step S2 during the current execution cycleof the program portion. The step S31 determines which of the segmentdata pieces each of the target models corresponds to.

With reference to FIG. 6, the step S31 refers to the position Bi(n−1)and relative speed (Vx, Vz) of each target model Bi which occur at theimmediately-previous execution cycle of the program portion. The stepS31 calculates an estimated current position Bi(n) of the target modelBi from the previous position Bi(n−1) and relative speed (Vx, Vz)thereof. Specifically, the estimated current position Bi(n) is equal tothe previous position Bi(n−1) plus the relative speed (Vx, Vz)multiplied by the scanning period. The step S31 sets an estimatedarrival zone BB centered at the estimated current position Bi(n) andextending around the target model in the estimated current positionBi(n). The estimated arrival zone BB is of a rectangular shape havingupper and lower sides parallel to the X axis, and left-hand andright-hand sides parallel to the Z axis. The upper and lower sides ofthe estimated arrival zone BB are spaced from the upper and lower sidesof the target model in the estimated current position Bi(n) at apredetermined interval ΔZo (different from or equal to the upper limitof the Z-axis-direction distances ΔZ used in the step S2). The left-handand right-hand sides of the estimated arrival zone BB are spaced fromthe left-hand and right-hand sides of the target model in the estimatedcurrent position Bi(n) at a predetermined interval ΔXo (different fromor equal to the upper limit of the X-axis-direction distances ΔX used inthe step S2). The step S31 searches the current segment data pieces forhit one representing a complete object at least partially contained inthe estimated arrival zone BB. The step S31 determines that the hitcurrent segment data piece corresponds to the target model Bi.

As shown in FIG. 5, a block S32 follows the step S31. The block S32updates data of each target model Bi. Specifically, in the presence of acurrent segment data piece corresponding to each target model Bi, theblock S32 upstates data of the target model Bi. After the block S32, theprogram advances to a step S33.

With reference to FIG. 7, the block S32 has a step S321 following thestep S31 (see FIG. 5). The step S321 determines whether a currentsegment data piece corresponding to each target model Bi is present orabsent. When the step S321 determines that a current segment data piececorresponding to the target model Bi is present, the program advancesfrom the step S321 to a step S322. The step S322 updates past data ofthe target model Bi in response to the corresponding current segmentdata. A step S323 subsequent to the step S322 updates current-positiondata of the target model Bi in response to the corresponding currentsegment data. When the step S321 determines that a current segment datapiece corresponding to the target model Bi is absent, the program exitsfrom the step S321 and then skips over the steps S322 and S323. Theabove-indicated sequence of the steps S321, S322, and S323 is executedfor each of target models. After the signal processing for all thetarget models has been completed, the program advances from the blockS32 to the step S33 (see FIG. 5).

With reference back to FIG. 5, the step S33 registers a new target modelor models. The step S33 selects one or ones out of the current segmentdata pieces which correspond to none of the target models. The step S33registers the selected current segment data piece or pieces as a newtarget model or models. The step S33 limits the number of new targetmodels to a prescribed number (for example, 8).

A block S34 follows the step S33. The block S34 implements a process ofmerging target models. A wrong target model is caused by, for example,noise. In the case where there are a correct target model and a wrongtarget model for one complete object, the block S34 deletes the wrongtarget model. After the block S34, the program advances to a step S35.

As shown in FIG. 8, the block S34 has a step S341 following the step S33(see FIG. 5). The step S341 determines whether or not the target modelshave at least one pair which should be merged. When the step S341determines that the target models have at least one pair which should bemerged, the program advances from the step S341 to a step S342.Otherwise, the program jumps from the step S341 to the step S35 (seeFIG. 5).

Conditions of two target models “A” and “B” which should be merged areas follows. A first condition is that as shown in FIG. 9, theX-direction range of one of the target models “A” and “B” is containedin the X-direction range of the other. A second conditions is that theZ-direction distance ZD between the centers of the target models “A” and“B” is smaller than a predetermined threshold value. The step S341judges whether or not the first and second conditions are satisfied.When the first and second conditions are satisfied, the step S341determines that the target models “A” and “B” should be merged. In thiscase, the program advances from the step S341 to the step S342, and aprocess of deleting one of the target models “A” and “B” and leaving theother is started. When the first and second conditions are notsatisfied, the step S341 determines that the target models “A” and “B”should not be merged. In this case, the program jumps from the step S341to the step S35 (see FIG. 5).

The step S342 determines whether or not the recognition types (thestationary-moving determination results) of the target models “A” and“B” are the same. When the recognition types of the target models “A”and “B” are the same, the program advances from the step S342 to a stepS343. Otherwise, the program advances from the step S342 to a step S350.

The step S350 deletes one of the target models “A” and “B”, and leavesthe other. Specifically, the step S350 leaves one of the target models“A” and “B” according to predetermined conditions {circle around (1)}and {circle around (2)} as follows.

The condition {circle around (1)}: In the case where one of the targetmodels “A” and “B” continues to be present for a prescribed timeinterval (for example, 2 seconds) or longer, the one is left.

The condition {circle around (2)}: In the absence of a target modelsatisfying the condition {circle around (1)}, one of the target models“A” and “B” is left which is longer in time interval during which thetarget model continues to be present.

After the step S350, the program advances to the step S35 (see FIG. 5).

The step S343 assigns the variables Pa(%) and Pb(%) to theimmediately-previous lane-sameness probabilities of the target models“A” and “B”, respectively. The step S343 refers to a predeterminedthreshold value X(%) for selection as an objective preceding vehicle.The step 3343 determines whether or not Pa(%)≧X(%) and Pb(%)≧X(%). Inaddition, the step S343 determines whether or not Pa(%)<X(%) andPb(%)<X(%). When Pa(%)≧X(%) and Pb(%)≧X(%) or when Pa(%)<X(%) andPb(%)<X(%), the program advances from the step S343 to a step S346.

Otherwise, the program advances from the step S343 to a step S344.

The step S344 determines whether or not Pa(%)>Pb(%). When Pa(%)>Pb(%),the program advances from the step S344 to a step S345. Otherwise, theprogram advances from the step S344 to a step S349.

The step S345 leaves the target model “A”. In other words, the step S345deletes the target model “B”. After the step S345, the program advancesto the step S35 (see FIG. 5).

The step S349 leaves the target model “B”. In other words, the step S349deletes the target model “A”. After the step S349, the program advancesto the step S35 (see FIG. 5).

The step S346 determines whether or not only one of the target models“A” and “B” is in a predetermined stable state. When only one of thetarget models “A” and “B” is in the predetermined stable state, theprogram advances from the step S346 to a step S347. Otherwise, theprogram advances from the step S346 to a step S348.

Specifically, the step S346 calculates the accelerations of the targetmodels “A” and “B” relative to the present vehicle. The step S346 refersto a prescribed usual range and a prescribed unusual range for each ofthe calculated accelerations. An acceleration in the prescribed unusualrange hardly occurs under ordinary traffic situations. The step S346compares each of the calculated acceleration with the prescribed usualrange and the prescribed unusual range. The step S346 judges a targetmodel to be in the predetermined stable state when the relatedcalculated acceleration is in the prescribed usual range. On the otherhand, the step S346 judges a target model to be not in the predeterminedstable state when the related calculated acceleration is in theprescribed unusual range. In more detail, the step S346 calculates theabsolute values of the accelerations of the target models “A” and “B”relative to the present vehicle. The step S346 compares each of thecalculated absolute values of the accelerations with a predeterminedreference value. The step S346 judges a target model to be in thepredetermined stable state when the calculated absolute value of therelated acceleration is equal to or smaller than the predeterminedreference value. On the other hand, the step S346 judges a target modelto be not in the predetermined stable state when the calculated absolutevalue of the related acceleration is greater than the predeterminedreference value.

The step S347 determines whether or not the target model “A” is in thepredetermined stable state. When the target model “A” is in thepredetermined stable state, the program advances from the step S347 tothe step S345 which leaves the target model “A”. Otherwise, the programadvances from the step S347 to the step S349 which leaves the targetmodel “B”.

The step S348 calculates a distance deviation related to the targetmodel “A” and a distance deviation related to the target model “B”. Thestep S348 compares the calculated distance deviations with each other.When the distance deviation related to the target model “A” is equal toor smaller than that related to the target model “B”, the programadvances from the step S348 to the step S345 which leaves the targetmodel “A”. Otherwise, the program advances from the step S348 to thestep S349 which leaves the target model “B”.

The estimated current position of a target model is calculated from theprevious position and relative speed thereof. The current position of atarget model is determined in the current execution cycle of the programportion. A distance deviation used in the step S348 means the differencebetween the estimated current position of a target model and theactually-determined current position thereof. With reference to FIG. 10,the step S348 refers the distances to the target models “A” and “B” andthe relative speeds of the target models “A” and “B” which occur in theimmediately-previous execution cycle of the program portion. The stepS348 calculates estimated current distances Az and Bz to the targetmodels “A” and “B” on the basis of the previous distances and relativespeeds thereof Specifically, the estimated current distance Az to thetarget model “A” is equal to the previous distance plus the previousrelative speed multiplied by the measurement period (the scanningperiod). Similarly, the estimated current distance Bz to the targetmodel “B” is equal to the previous distance plus the previous relativespeed multiplied by the measurement period (the scanning period). Duringthe current execution cycle of the program portion, the step S348provisionally combines the target models “A” and “B” into a target model“C”. The step S348 determines the current distance Cz related to thetarget model “C”.

The step S348 calculates |Cz−Az|, that is, the absolute value of thedifference between the distance Cz and the distance Az related to thetarget model “A”. The calculated absolute value |Cz−Az| is defined asthe distance deviation related to the target model “A”. Also, the stepS348 calculates |Cz−Bz|, that is, the absolute value of the differencebetween the distance Cz and the distance Bz related to the target model“B”. The calculated absolute value |Cz−Bz| is defined as the distancedeviation related to the target model “B”.

With reference back to FIG. 5, the step S35 computes the probability(the lane-sameness probability) P that the lanes along which the presentvehicle and the complete object (the preceding vehicle) represented byeach target model are traveling respectively are the same. After thestep S35, the program exits from the block S3 (see FIG. 3) and then thecurrent execution cycle of the program portion ends.

The step S35 will further be described below. The step S35 computes theradius R of curvature of the road on the basis of the vehicle speed V,the steering angle, and the yaw rate. For each of the complete objects(the target models), the step S35 computes the instantaneous probability(the instantaneous lane-sameness probability) Po that the completeobject is traveling along the lane same as the lane along which thepresent vehicle is moving. The computation of the instantaneouslane-sameness probability Po is based on the computed road curvatureradius R, and the central position (Xo, Zo) and size of the completeobject. Specifically, as shown in FIG. 11, the step S35 converts thecoordinates (Xo, Zo) of the central position of each complete object(each target model) into the coordinates (X, Z) thereof which occur onthe assumption that the present vehicle is traveling along a straightroad. In more detail, the step S35 converts the coordinate values Xo andZo into the coordinate values X and Z according to the followingequations.

X=Xo−(Zo ²/2R)  (1)

Z=Zo  (2)

The equations (1) and (2) are made on the basis of approximation usingthe assumption that the absolute value of the coordinate value Xo issignificantly smaller than the road curvature radius R and thecoordinate value Zo (|Xo|<<|R| and |Xo|<<Z). The step S35 converts thesize of each complete object (each target object) in accordance with theabove-indicated conversion of the central position of the completeobject. In the case where the laser radar sensor 5 is significantlydistant from the center of the body of the present vehicle, the X-Zcoordinate system is corrected so that the origin thereof will coincidewith the vehicle center. The ROM within the ECU 3 stores datarepresenting a map of a predetermined relation among the instantaneouslane-sameness probability Po, the coordinate values X and Z, and theconversion-resultant complete-object size. The step S35 derives theinstantaneous lane-sameness probability Po by accessing the map inresponse to the coordinate values X and Z and the conversion-resultantcomplete-object size.

FIG. 12 shows an example of the map for the instantaneous lane-samenessprobability Po. In FIG. 12, the X axis corresponds to the width-wisedirection of the present vehicle while the Z axis corresponds to thelongitudinal forward direction of the present vehicle (that is, thedirection along which the present vehicle is traveling). With referenceto FIG. 12, there are separate regions a0, b0, c0, d0, e0, a1, b1, c1,d1, and e1. The regions a0 and a1 are symmetrical with respect to the Zaxis. The regions b0 and b1 are symmetrical with respect to the Z axis.The regions c0 and c1 are symmetrical with respect to the Z axis. Theregions d0 and d1 are symmetrical with respect to the Z axis. Theregions e0 and e1 are symmetrical with respect to the Z axis. Aninstantaneous lane-sameness probability Po of 80% is assigned to theregions a0 and a1. An instantaneous lane-sameness probability Po of 60%is assigned to the regions b0 and b1. An instantaneous lane-samenessprobability Po of 30% is assigned to the regions c0 and c1. Aninstantaneous lane-sameness probability Po of 100% is assigned to theregions d0 and d1. An instantaneous lane-sameness probability Po of 0%is assigned to the regions e0 and e1. The setting of the regions a0, b0,c0, d0, e0, a1, b1, c1, d1, and e1, and the assignment of probabilityvalues thereto are decided in consideration of the results ofexperiments including actual measurement. Preferably, the regions d0 andd1 are chosen in view of the case where another vehicle suddenly comesinto a zone immediately preceding the present vehicle. There areboundaries La0, Lb0, Lc0, and Ld0 among the regions a0, b0, c0, d0, ande0. The boundaries La0, Lb0, Lc0, and Ld0 are given according to thefollowing equations.

La0: X=0.70+(1.75−0.70)·(Z/100)²  (3)

Lb0: X=0.70+(3.50−0.70)·(Z/100)²  (4)

Lc0: X=1.00+(5.00−1.00)·(Z/100)²  (5)

Ld0: X=1.50·(1−Z/60)  (6)

There are boundaries La1, Lb1, Lc1, and Ld1 among the regions a1, b1,c1, d1, and e1. The boundaries La0 and La1 are symmetrical with respectto the Z axis. The boundaries Lb0 and Lb1 are symmetrical with respectto the Z axis. The boundaries Lc0 and Lc1 are symmetrical with respectto the Z axis. The boundaries Ld0 and Ld1 are symmetrical with respectto the Z axis. The boundaries La1, Lb1, Lc1, and Ld1 are decided byreferring to the symmetrical relation with the boundaries La0, Lb0, Lc0,and Ld0.

The equations (3), (4), (5), and (6) are determined on the basis ofgeneral equations as follows.

La0: X=A1+B1·(Z/C1)²  (7)

Lb0: X=A2+B2·(Z/C2)²  (8)

Lc0: X=A3+B3·(Z/C3)²  (9)

Ld0: X=A4·(B4−Z/C4)  (10)

The values of the parameters A1-A4, B1-B4, and C1-C4 are decided viaexperiments, and are chosen so as to have the following relations.

A1≦A2≦A3≦A4  (11)

B1≦B2≦B3 and B4=1  (12)

C1=C2=C3  (13)

It is more preferable that the boundaries La0, Lb0, Lc0, La1, Lb1, andLc1 are accorded with circular arcs respectively. It is more preferablethat the boundaries Ld0 and Ld1 are accorded with circular arcs oroutwardly-convex parabolas.

For each of the complete objects (the target models), the step S35applies the conversion-resultant coordinate values X and Z and thecomplete-object conversion-resultant size to the map in FIG. 12, andthereby determines the instantaneous lane-sameness probability Po.{circle around (1)} When at least part of a complete object is in or onthe regions d0 and d1, the step S35 sets the instantaneous lane-samenessprobability Po for the complete object to 100%. {circle around (2)} Whenthe central position (X, Z) of a complete object is in the regions a0and a1, the step S35 sets the instantaneous lane-sameness probability Pofor the complete object to 80%. {circle around (3)} When the centralposition (X, Z) of a complete object is in the regions b0 and b1, thestep S35 sets the instantaneous lane-sameness probability Po for thecomplete object to 60%. {circle around (4)} the central position (X, Z)of a complete object is in the regions c0 and c1, the step S35 sets theinstantaneous lane-sameness probability Po for the complete object to30%. For a complete object which satisfies none of thepreviously-indicated conditions {circle around (1)}, {circle around(2)}, {circle around (3)}, and {circle around (4)}, the step S35 setsthe instantaneous lane-sameness probability Po to 0%.

For each of the complete objects (the target models), the step S35subjects the instantaneous lane-sameness probability Po to a filteringprocess corresponding to a smoothing or low-pass filtering process. Inmore detail, for each of the complete objects, the step S35 calculates acurrent filtering-resultant lane-sameness probability (a current finallane-sameness probability) P_(n) from the instantaneous lane-samenessprobability Po according to the following equation.

P _(n) =P _(n−1) ·α+Po·(1−α)  (14)

where P_(n−1) denotes an immediately-previous filtering-resultantlane-sameness probability (an immediately-previous final lane-samenessprobability), and “α” denotes a parameter depending on the distance Z tothe complete object from the present vehicle. With reference to FIG. 13,the ROM within the ECU 3 stores data representing a map of apredetermined relation between the parameter “α” and the distance Z. Foreach of the complete objects (the target models), the step S35 derivesthe value of the parameter “α” by accessing the map in response to thedistance Z. In FIG. 13, the parameter “α” remains equal to 0.85 as thedistance Z increases from 0 m to 20 m. The parameter “α” linearlyincreases from 0.85 to 0.96 as the distance Z increases from 20 m to 100m. The parameter “α” remains equal to 0.96 as the distance Z increasesfrom 100 m. The initial value of the current filtering-resultantlane-sameness probability (the current final lane-sameness probability)P_(n) is equal to 0%.

The step S35 in FIG. 5 corresponds to the object recognition block 43 inFIG. 2. Data of target models which contain data pieces representingcurrent filtering-resultant lane-sameness probabilities are transferredfrom the object recognition block 43 to the preceding-vehicledetermination block 53 in FIG. 2. An example of operation of thepreceding-vehicle determination block 53 is as follows. Thepreceding-vehicle determination block 53 selects, from all the targetmodels, ones related to current filtering-resultant lane-samenessprobabilities equal to or higher than a predetermined threshold value TH(for example, 50%). The preceding-vehicle determination block 53 setsthe selected target models as candidate ones. Then, thepreceding-vehicle determination block 53 compares the distances Zrelated to the respective candidate target models to find the smallestof the distances Z. The preceding-vehicle determination block 53 selectsone out of the candidate target models which corresponds to the smallestdistance Z. The preceding-vehicle determination block 53 sets theselected target model as an objective preceding vehicle. Thepreceding-vehicle determination block 53 informs the inter-vehicledistance control and warning determination block 55 of the objectivepreceding vehicle and the related filtering-resultant lane-samenessprobability.

As previously mentioned, the step S343 in FIG. 8 refers to thepredetermined threshold value X(%) for selection as an objectivepreceding vehicle. The predetermined threshold value X(%) may be equalto the predetermined threshold value TH used in the preceding-vehicledetermination block 53.

The laser radar sensor 5 corresponds to radar means. The objectrecognition block 43 provided by the ECU 3 corresponds to recognizingmeans. The steps in FIGS. 3, 5, 7, and 8 correspond to the function ofthe recognizing means.

The vehicle control apparatus has advantages as mentioned below. In thecase where the recognition types (the stationary-moving determinationresults) of two target models which should be merged are the same, theprogram reaches the step S343 through the steps S341 and S342 in FIG. 8.The step S343 and the later steps S344, S345, and S349 select and leaveone of the two target models as a candidate preceding vehicle on thebasis of the lane-sameness probabilities of the two target models.Accordingly, it is possible to prevent an objective preceding vehiclefrom being lost. When selection of one of the two target models on thebasis of the lane-sameness probabilities is difficult, the programadvances from the step S343 to the step S346 in FIG. 8. The step S346and the later steps S347, S345, and S349 select and leave one of the twotarget models which is in the predetermined stable state. Therefore, itis possible to enhance the reliability of the determination about anobjective preceding vehicle. When selection of one of the two targetmodels on the basis of the predetermined stable state is difficult, theprogram advances from the step S346 to the step S348 in FIG. 8. The stepS348 and the later steps S345 and S349 select and leave one of the twotarget models which relates to a smaller distance deviation.Accordingly, it is possible to enhance the accuracy of the determinationabout an objective preceding vehicle.

Second Embodiment

A second embodiment of this invention is similar to the first embodimentthereof except that one of two target models which should be merged isselected and left on the basis of one among 1) the lane-samenessprobabilities, 2) the predetermined stable state, and 3) the distancedeviations.

Third Embodiment

A third embodiment of this invention is similar to the first embodimentthereof except that one of two target models which should be merged isselected and left on the basis of two among 1) the lane-samenessprobabilities, 2) the predetermined stable state, and 3) the distancedeviations.

Fourth Embodiment

A fourth embodiment of this invention is similar to the first embodimentthereof except for design changes mentioned later. In the fourthembodiment of this invention, the laser radar sensor 5 (see FIG. 1) ismodified to cyclically deflect the forward laser beam in both thewidth-wise direction (the X-axis direction) and the height-wisedirection (the Y-axis direction) with respect to the present vehicle toperiodically scan a given three-dimensional region in front of thepresent vehicle. Therefore, the laser radar sensor 5 detects thethree-dimensional position (X, Y, Z) of an object in the giventhree-dimensional region.

The given three-dimensional region corresponds to a three-dimensionaldetection area monitored by the laser radar sensor 5. Thethree-dimensional detection area is scanned by the forward laser beam ona line-by-line scanning basis. During every scanning period (every frameperiod), the direction of the forward laser beam is changed a unit-angleby a unit-angle along the width-wise direction (the X-axis direction)with respect to the present vehicle.

The width-wise unit angle corresponds to, for example, 0.15 degrees.Also, the direction of the forward laser beam is changed a unit-angle bya unit-angle along the height-wise direction (the Y-axis direction) withrespect to the present vehicle. The height-wise unit angle correspondsto, for example, 0.7 degrees. The three-dimensional detection area hasan angular range of, for example, about 16 degrees which extends in thewidth-wise direction (the X-axis direction). In this case, thewidth-wise angular range of the three-dimensional detection areacorresponds to 105 image points or pixels (105 multiplied by 0.15degrees equals about 16 degrees). The three-dimensional detection areahas an angular range of, for example, about 4 degrees which extends inthe height-wise direction (the Y-axis direction). In this case, theheight-wise angular range of the three-dimensional detection areacorresponds to 6 lines (6 multiplied by 0.7 degrees equals about 4degrees). Thus, one frame is composed of 630 image points or pixels (105image points multiplied by 6 lines).

During every scanning period (every frame period), the three-dimensionaldetection area is scanned by the forward laser beam along the firstscanning line, that is, the uppermost scanning line. Subsequently, thethree-dimensional detection area is scanned by the forward laser beamalong the second scanning line. Then, the three-dimensional detectionarea is scanned by the forward laser beam along the third and laterscanning lines. Finally, the three-dimensional detection area is scannedby the forward laser beam along the sixth scanning line, that is, thelowermost scanning line. Thus, during every scanning period, the laserradar sensor 5 generates and outputs measurement data corresponding to630 image points or pixels.

In the fourth embodiment of this invention, the step S2 (see FIG. 3) ismodified to implement processes as follows. The step S2 searches thedetected point-like object parts for close ones which are spaced byX-axis-direction distances ΔX of 0.2 m or less and Z-axis-directiondistances ΔZ of 2 m or less. The step S2 combines or unifies the closepoint-like object parts into a pre-segment corresponding to atwo-dimensional object part. There can be a plurality of pre-segments.The step S2 searches the pre-segments for close ones which are spaced byY-axis-direction distances ΔY of a predetermined reference value orless. The step S2 combines or unifies the close pre-segments into asegment corresponding to a detected complete object. There can be aplurality of segments.

In the fourth embodiment of this invention, the step S341 (see FIG. 8)is modified to additionally implement the following processes. The stepS341 accesses pieces of pre-segment data which correspond to respectivetarget models. The step S341 compares the height-wise positionsrepresented by the pre-segment data pieces. The step S341 determineswhether or not two target models should be merged on the basis of theheight-wise positions represented by the related pre-segment datapieces. Specifically, the step S341 determines that two target modelsshould be merged when the height-wise positions represented by therelated pre-segment data piece are equal to each other.

Fifth Embodiment

A fifth embodiment of this invention is similar to the first embodimentthereof except that the conversion of the distance and angle data ofpolar coordinates into measurement data of X-Z orthogonal coordinates isimplemented by the laser radar sensor 5 instead of the objectrecognition block 43 provided by the ECU 3.

Sixth Embodiment

A sixth embodiment of this invention is similar to the first embodimentthereof except for a design change mentioned later. The laser radarsensor 5 which employs the laser beam is used as the radar means. In thesixth embodiment of this invention, the radar means is modified to use aradio wave beam, a millimeter wave beam, or an ultrasonic beam. The typeof the scanning process by the radar means may differ from that in thefirst embodiment of this invention. In the case where the radar meansuses a Doppler radar or an FMCW radar employing a millimeter wave beam,information of a distance to a preceding vehicle and information of arelative speed of the preceding vehicle are simultaneously derived froman echo wave beam (a return wave beam). Thus, in this case, it isunnecessary to execute a step of calculating a relative speed fromdistance information.

What is claimed is:
 1. A method of recognizing objects located ahead ofa vehicle on the basis of reflected waves resulting from a transmissionwave emitted ahead of the vehicle within a predetermined angular range,the method comprising the steps of: calculating positions of theobjects; calculating a lane-sameness probability for each of the objectsthat the object and the vehicle are on a same lane; generating objectinformation pieces corresponding to the objects respectively, the objectinformation pieces representing the calculated positions of the objectsand the calculated lane-sameness probabilities for the objects;determining whether or not at least two objects among the objects becomesubstantially equal in position; in cases where it is determined that atleast two objects become substantially equal in position, recognizingthe at least two objects as a single object; selecting one from the atleast two objects which relates to a calculated lane-samenessprobability equal to or higher than a predetermined value; and causingsaid single object to take over an object information piececorresponding to the selected object.
 2. A method of recognizing objectslocated ahead of a vehicle on the basis of reflected waves resultingfrom a transmission wave emitted ahead of the vehicle within apredetermined angular range, the method comprising the steps of:calculating positions of the objects; determining whether or not arecognition state of each of the objects is stable; generating objectinformation pieces corresponding to the objects respectively, the objectinformation pieces representing the calculated positions of the objectsand whether or not the recognition states of the objects are stable;determining whether or not at least two objects among the objects becomesubstantially equal in position; in cases where it is determined that atleast two objects become substantially equal in position, recognizingthe at least two objects as a single object; selecting one from the atleast two objects whose recognition state is determined to be stable;and causing said single object to take over the object information piececorresponding to the selected object.
 3. A method of recognizing objectslocated ahead of a vehicle on the basis of reflected waves resultingfrom a transmission wave emitted ahead of the vehicle within apredetermined angular range, the method comprising the steps of:periodically calculating positions of the objects; estimating currentpositions of the objects on the basis of previously calculated positionsthereof; calculating deviations between the estimated current positionsof the objects and currently calculated positions thereof; generatingobject information pieces corresponding to the objects respectively, theobject information pieces representing the calculated positions of theobjects and the calculated deviations related to the objects;determining whether or not at least two objects among the objects becomesubstantially equal in position; in cases where it is determined that atleast two objects become substantially equal in position, recognizingthe at least two objects as a single object; selecting one from the atleast two objects which relates to a smallest calculated deviation; andcausing said single object to take over the object information piececorresponding to the selected object.
 4. An object recognition apparatuscomprising: radar means for applying a transmission wave to apredetermined angular range in a direction ahead of a vehicle, anddetecting reflected waves which result from reflections of thetransmission wave; and recognizing means for recognizing objects locatedahead of the vehicle on the basis of results of the detecting by theradar means; wherein the recognizing means comprises: 1) first means forcalculating positions of the recognized objects; 2) second means fordetermining whether or not a recognition state of each of the recognizedobjects is stable; 3) third means for generating object informationpieces corresponding to the recognized objects respectively, the objectinformation pieces representing the calculated positions of therecognized objects and whether or not the recognition states of therecognized objects are stable; 4) fourth means for determining whetheror not at least two objects among the recognized objects becomesubstantially equal in position; 5) fifth means for, in cases where thefourth means determines that at least two objects become substantiallyequal in position, recognizing the at least two objects as a singleobject; 6) sixth means for selecting one from the at least two objectswhose recognition state is determined to be stable; and 7) seventh meansfor causing said single object to take over the object information piececorresponding to the object selected by the sixth means.
 5. An objectrecognition apparatus as recited in claim 4, wherein the recognizingmeans further comprises: eighth means for determining whether or not arecognition state of each of the recognized objects is stable; ninthmeans for adding results of the determining by the eighth means to theobject information pieces; tenth means for, either in cases where aplurality of objects among the at least two objects relate to calculatedlane-sameness probabilities equal to or higher than the predeterminedvalue or in cases where a plurality of objects among the at least twoobjects relate to calculated lane-sameness probabilities less than thepredetermined value, selecting one from the at least two objects whoserecognition state is determined to be stable; and eleventh means forcausing said single object to take over the object information piececorresponding to the object selected by the tenth means.
 6. An objectrecognition apparatus as recited in claim 4, wherein the recognizingmeans further comprises: eighth means for estimating current positionsof the recognized objects on the basis of previously calculatedpositions thereof; ninth means for calculating deviations between theestimated current positions of the recognized objects and currentlycalculated positions thereof; tenth means for adding the deviationscalculated by the ninth means to the object information pieces; eleventhmeans for, either in cases where a plurality of objects among the atleast two objects relate to calculated lane-sameness probabilities equalto or higher than the predetermined value or in cases where a pluralityof objects among the at least two objects relate to calculatedlane-sameness probabilities less than the predetermined value, selectingone from the at least two objects which relates to a smallest calculateddeviation; and twelfth means for causing said single object to take overthe object information piece corresponding to the object selected by theeleventh means.
 7. An object recognition apparatus as recited in claim4, wherein the recognizing means further comprises: eighth means fordetermining whether or not a recognition state of each of the recognizedobjects is stable; ninth means for adding results of the determining bythe eighth means to the object information pieces; tenth means forestimating current positions of the recognized objects on the basis ofpreviously calculated positions thereof; eleventh means for calculatingdeviations between the estimated current positions of the recognizedobjects and currently calculated positions thereof; twelfth means foradding the deviations calculated by the eleventh means to the objectinformation pieces; thirteenth means for, either in cases where aplurality of objects among the at least two objects relate to calculatedlane-sameness probabilities equal to or higher than the predeterminedvalue and only one of the at least two objects has a recognition statedetermined to be stable or in cases where a plurality of objects amongthe at least two objects relate to calculated lane-samenessprobabilities less than the predetermined value and only one of the atleast two objects has a recognition state determined to be stable,selecting one from the at least two objects whose recognition state isdetermined to be stable; fourteenth means for causing said single objectto take over the object information piece corresponding to the objectselected by the thirteenth means; fifteenth means for, either in caseswhere a plurality of objects among the at least two objects relate tocalculated lane-sameness probabilities equal to or higher than thepredetermined value and two or more of the at least two objects haverecognition states determined to be stable or in cases where a pluralityof objects among the at least two objects relate to calculatedlane-sameness probabilities less than the predetermined value and two ormore of the at least two objects have recognition states determined tobe stable, selecting one from the at least two objects which relates toa smallest calculated deviation; and sixteenth means for causing saidsingle object to take over the object information piece corresponding tothe object selected by the fifteenth means.
 8. An object recognitionapparatus comprising: radar means for emitting a transmission wavewithin a predetermined range of directions ahead of a vehicle, anddetecting reflected waves which result from reflections of thetransmission wave; and recognizing means for recognizing objects locatedahead of the vehicle on the basis of results of the detecting by theradar means; wherein the recognizing means comprises: 1) first means forcalculating positions of the recognized objects; 2) second means forcalculating a lane-sameness probability for each of the recognizedobjects that the object and the vehicle are on a same lane; 3) thirdmeans for generating object information pieces corresponding to therecognized objects respectively, the object information piecesrepresenting the calculated positions of the recognized objects and thecalculated lane-sameness probabilities for the recognized objects; 4)fourth means for determining whether or not at least two objects amongthe recognized objects become substantially equal in position; 5) fifthmeans for, in cases where the fourth means determines that at least twoobjects become substantially equal in position, recognizing the at leasttwo objects as a single object; 6) sixth means for selecting one fromthe at least two objects which relates to a calculated lane-samenessprobability equal to or higher than a predetermined value; and 7)seventh means for causing said single object to take over an objectinformation piece corresponding to the object selected by the sixthmeans.
 9. An object recognition apparatus as recited in claim 8, whereinthe recognizing means further comprises: eighth means for estimatingcurrent positions of the recognized objects on the basis of previouslycalculated positions there of; ninth means for calculating deviationsbetween the estimated current positions of the recognized objects andcurrently calculated positions thereof; tenth means for adding thedeviations calculated by the ninth means to the object informationpieces; eleventh means for, either in cases where a plurality of objectsamong the at least two objects relate to calculated lane-samenessprobabilities equal to or higher than the predetermined value and two ormore of the at least two objects have recognition states determined tobe stable or in cases where a plurality of objects among the at leasttwo objects relate to calculated lane-sameness probabilities less thanthe predetermined value and two or more of the at least two objects haverecognition states determined to be stable, selecting one from the atleast two objects which relates to a smallest calculated deviation; andtwelfth means for causing said single object to take over the objectinformation piece corresponding to the object selected by the eleventhmeans.
 10. An object recognition apparatus as recited in claim 8,wherein the second means in the recognizing means comprises means forcalculating an acceleration of each of the recognized objects relativeto the vehicle, means for judging whether or not the calculatedacceleration is in a predetermined range hardly occurring under usualtraffic conditions, means for, when the calculated acceleration isjudged to be in the predetermined range, determining that a recognizedstate of the related object is not stable, and means for, when thecalculated acceleration is judged to be not in the predetermined range,determining that a recognition state of the related object is stable.11. An object recognition apparatus comprising: radar means for emittinga transmission wave within a predetermined range of directions ahead ofa vehicle, and detecting reflected waves which result from reflectionsof the transmission wave; and recognizing means for recognizing objectslocated ahead of the vehicle on the basis of results of the detecting bythe radar means; wherein the recognizing means comprises: 1) first meansfor periodically calculating positions of the recognized objects; 2)second means for estimating current positions of the recognized objectson the basis of the previously calculated positions thereof; 3) thirdmeans for generating object information pieces corresponding to therecognized objects respectively, the object information piecesrepresenting the calculated positions of the recognized objects andwhether or not the recognition states of the recognized objects arestable; 4) fourth means for generating object information piecescorresponding to the recognized objects respectively, the objectinformation pieces representing the calculated positions of therecognized objects and the calculated deviations related to therecognized objects; 5) fifth means for determining whether or not atleast two objects among the recognized objects become substantiallyequal in position; 6) sixth means for, in cases where the fifth meansdetermines that at least two objects become substantially equal inposition, recognizing the at least two objects as a single object; 7)seventh means for selecting one from the at least two object whichrelates to a smallest calculated deviation; and 8) eighth means forcausing said single object to take over the object information piececorresponding to the object selected by the seventh means.
 12. Arecording medium storing a program for controlling a computer torecognize objects located ahead of a vehicle based on reflected radarwaves, the computer operating as a recognizing means comprising: firstmeans for calculating positions of the recognized objects; second meansfor determining whether or not a recognition state of each of therecognized objects is stable; third means for generating objectinformation pieces corresponding to the recognized objects respectively,the object information pieces representing the calculated positions ofthe recognized objects and whether or not the recognition states of therecognized objects are stable; fourth means for determining whether ornot at least two objects among the recognized objects becomesubstantially equal in position; fifth means for, in cases where thefourth means determines that at least two objects become substantiallyequal in position, recognizing the at least two objects as a singleobject; sixth means for selecting one from the at least two objectswhose recognition state is determined to be stable; and seventh meansfor causing said single object to take over the object information piececorresponding to the object selected by the sixth means.
 13. Therecording medium storing a program for controlling a computer torecognize objects located ahead of a vehicle based on reflected radarwaves as recited in claim 12, wherein the recognizing means furthercomprises; eighth means for determining whether or not a recognitionstate of each of the recognized objects is stable; ninth means foradding results of the determining by the eighth means to the objectinformation pieces; tenth means for, either in cases where a pluralityof objects among the at least two objects relate to calculatedlane-sameness probabilities equal to or higher than the predeterminedvalue or in cases where plurality of objects among the at least twoobjects relate to calculated lane-sameness probabilities less than thepredetermined valued, selecting one from the at least two objects whoserecognition state is determined to be stable; and eleventh means forcausing said single object to take over the object information piececorresponding to the object selected by the tenth means.
 14. Therecording medium storing a program for controlling a computer torecognize objects located ahead of a vehicle based on reflected radarwaves as recited in claim 12, wherein the recognizing means furthercomprises: eighth means for estimating current positions of therecognized objects on the basis of previously calculated positionsthereof; ninth means for calculating deviations between the estimatedcurrent positions of the recognized objects and currently calculatedpositions thereof; tenth means for adding the deviations calculated bythe ninth means to the object information pieces; eleventh means for,either in cases where a plurality of objects among the at least twoobjects relate to calculated lane-sameness probabilities equal to orhigher than the predetermined value or in cases where a plurality ofobjects among the at least two objects which relates to a smallestcalculated deviation; and twelfth means for causing said single objectto take over the object information piece corresponding to the objectselected by the eleventh means.
 15. The recording medium storing aprogram for controlling a computer to recognize objects located ahead ofa vehicle based on reflected radar waves as recited in claim 12, whereinthe recognizing means further comprises: eighth means for determiningwhether or not a recognition sate of each of the recognized objects isstable; ninth means for adding results of the determining by the eighthmeans to the object information pieces; tenth means for estimatingcurrent positions of the recognized objects on the basis of previouslycalculated positions thereof; eleventh means for calculating deviationsbetween the estimated current positions of the recognized objects andcurrently calculated positions thereof; twelfth means for adding thedeviations calculated by the eleventh means to the object informationpieces; thirteenth means for, either in cases where a plurality ofobjects among the at least two objects relate to calculatedlane-sameness probabilities equal to or higher than the predeterminedvalue and only one of the at least two objects has recognition statedetermined to be stable, selecting one from the at least two objectswhose recognition state is determined to be stable; fourteenth means forcausing said single object to take over the object information piececorresponding to the object selected by the thirteenth means; fifteenthmeans for, either in cases where a plurality of objects among the atleast two objects relate to calculated lane-sameness probabilities equalto or higher than the predetermined value and two or more of the leasttwo objects have recognition states determined to be stable or in caseswhere a plurality of objects among the at least two objects relate tocalculated lane-sameness probabilities less than the predetermined valueand two or more of the at least two objects have recognition statesdetermined to be stable, selection one from the at least two objectswhich relates to a smallest calculated deviation; and sixteenth meansfor causing said single object to take over the object information piececorresponding to the object selected by the fifteenth means.
 16. Therecording medium storing a program for controlling a computer torecognize objects located ahead of a vehicle based on reflected radarwaves as recited in claim 12, wherein the second means in therecognizing means comprises: means for calculating an acceleration ofeach of the recognized objects relative to the vehicle, means forjudging whether or not the calculated acceleration is in a predeterminedrange hardly occurring under usual traffic conditions, means for, whenthe calculated acceleration is judged to be in the predetermined range,determining that a recognized state of the related object is not stable,and means for, when the calculated acceleration is judged to be not inthe predetermined range, determining that a recognition state of therelated object is stable.